Concurrent image and corresponding multi-channel auxiliary data generation for a generative model

ABSTRACT

Systems and techniques for providing concurrent image and corresponding multi-channel auxiliary data generation for a generative model are presented. In one example, a system generates synthetic multi-channel data associated with a synthetic version of imaging data. The system also predicts multi-channel imaging data and the synthetic multi-channel data with a first predicted class set or a second predicted class set. Furthermore, the system employs the first predicted class set or the second predicted class set for the synthetic multi-channel data to train a generative adversarial network model.

RELATED APPLICATION

The subject patent application is a continuation of, and claims priorityto, U.S. patent application Ser. No. 16/368,101, filed Mar. 28, 2019,and entitled “CONCURRENT IMAGE AND CORRESPONDING MULTI-CHANNEL AUXILIARYDATA GENERATION FOR A GENERATIVE MODEL,” the entirety of whichapplication is hereby incorporated by reference herein.

TECHNICAL FIELD

This disclosure relates generally to artificial intelligence.

BACKGROUND

Artificial Intelligence (AI) can be employed for classification and/oranalysis of digital images. For instance, AI can be employed for imagerecognition. In certain technical applications, AI can be employed forgenerative modeling such as, for example, a generative adversarialnetwork, to learn data distributions. However, training a generativemodel and/or generating high quality synthetic images is generallydifficult to achieve. For example, accuracy and/or efficiency ofpixel-level annotation for synthetic images associated with generativemodels is generally difficult to achieve. As such, conventionalartificial intelligence techniques can be improved.

SUMMARY

The following presents a simplified summary of the specification inorder to provide a basic understanding of some aspects of thespecification. This summary is not an extensive overview of thespecification. It is intended to neither identify key or criticalelements of the specification, nor delineate any scope of the particularimplementations of the specification or any scope of the claims. Itssole purpose is to present some concepts of the specification in asimplified form as a prelude to the more detailed description that ispresented later.

According to an embodiment, a system includes a multi-channel generatorcomponent, a discriminator component and a training component. Themulti-channel generator component generates synthetic multi-channel dataassociated with a synthetic version of imaging data. The discriminatorcomponent predicts multi-channel imaging data and the syntheticmulti-channel data with a first predicted class set or a secondpredicted class set. The training component employs the first predictedclass set or the second predicted class set for the syntheticmulti-channel data to train a generative adversarial network model.

According to another embodiment, a method is provided. The methodcomprises generating synthetic multi-channel data associated with asynthetic version of imaging data. The method also comprises predictingmulti-channel imaging data and the synthetic multi-channel data with afirst predicted class set or a second predicted class set. Furthermore,the method comprises training a generative adversarial network modelbased on the first predicted class set or the second predicted class setfor the synthetic multi-channel data.

According to yet another embodiment, a computer readable storage deviceis provided. The computer readable storage device comprises instructionsthat, in response to execution, cause a system comprising a processor toperform operations, comprising generating synthetic multi-channel dataassociated with a synthetic version of imaging data. The processor alsoperforms operations, comprising predicting multi-channel imaging dataand the synthetic multi-channel data with a first predicted class set ora second predicted class set. The processor also performs operations,comprising training a generative adversarial network model based on thefirst predicted class set or the second predicted class set for thesynthetic multi-channel data.

The following description and the annexed drawings set forth certainillustrative aspects of the specification. These aspects are indicative,however, of but a few of the various ways in which the principles of thespecification may be employed. Other advantages and novel features ofthe specification will become apparent from the following detaileddescription of the specification when considered in conjunction with thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Numerous aspects, implementations, objects and advantages of the presentinvention will be apparent upon consideration of the following detaileddescription, taken in conjunction with the accompanying drawings, inwhich like reference characters refer to like parts throughout, and inwhich:

FIG. 1 illustrates a high-level block diagram of an example generativemodeling component, in accordance with various aspects andimplementations described herein;

FIG. 2 illustrates a high-level block diagram of another examplegenerative modeling component, in accordance with various aspects andimplementations described herein;

FIG. 3 illustrates a high-level block diagram of yet another examplegenerative modeling component, in accordance with various aspects andimplementations described herein;

FIG. 4 illustrates a high-level block diagram of yet another examplegenerative modeling component, in accordance with various aspects andimplementations described herein;

FIG. 5 illustrates a system that includes an example generative modelingbased inferencing component and an example medical imaging diagnosisprocess, in accordance with various aspects and implementationsdescribed herein;

FIG. 6 illustrates an example system associated with a generativenetwork associated with training, in accordance with various aspects andimplementations described herein;

FIG. 7 illustrates an example system associated with a multi-channelgenerator for inferencing, in accordance with various aspects andimplementations described herein;

FIG. 8 illustrates an example system associated with post-processing forinferencing and/or binary mask generation, in accordance with variousaspects and implementations described herein;

FIG. 9 depicts a flow diagram of an example method for providingconcurrent image and corresponding multi-channel auxiliary datageneration for a generative model, in accordance with various aspectsand implementations described herein;

FIG. 10 is a schematic block diagram illustrating a suitable operatingenvironment; and

FIG. 11 is a schematic block diagram of a sample-computing environment.

DETAILED DESCRIPTION

Various aspects of this disclosure are now described with reference tothe drawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of one or more aspects. It should beunderstood, however, that certain aspects of this disclosure may bepracticed without these specific details, or with other methods,components, materials, etc. In other instances, well-known structuresand devices are shown in block diagram form to facilitate describing oneor more aspects.

Systems and techniques that provide concurrent image and correspondingmulti-channel auxiliary data generation for a generative model arepresented. For example, a novel generative adversarial network isdisclosed herein. The novel generative adversarial network can include,during a training phase, a multi-channel generator that producesmulti-channel data and a discriminator that receives in multi-channeldata to classify real data or synthetic data. Other information such assegment information, etc. can be encoded in another channel so thatlearning can be concurrently performed during training. Syntheticmulti-channel data can also be generated during an inferencing phase. Inan embodiment, the novel generative adversarial network can employ amulti-channel generator associated with a convolutional neural networkto generate a synthetic image and two or more segmentations for thesynthetic image. The novel generative adversarial network can alsoemploy a discriminator associated with a convolutional neural network toclassify real images from multi-channel data related to the syntheticimage and the two or more segmentations for the synthetic image. In anaspect, synthetic images for a generative adversarial network can beannotated by concurrently generating corresponding multi-channelauxiliary data. In another aspect, a generative adversarial network canbe trained using a loss function that is generated based onmulti-channel data generation to tune the generative adversarial networkaccordingly. In certain embodiments, the novel generative adversarialnetwork can employ concurrent medical image and correspondingmulti-channel auxiliary data to improve annotation related to diseasedetection. For example, the novel generative adversarial network canemploy concurrent medical image and corresponding multi-channelauxiliary data without the need of a radiologist to annotate at a pixellevel for the synthetic imagery. As such, by employing the novelgenerative adversarial network as described herein, detection and/orlocalization of one or more features associated with image data (e.g.,detection and/or localization of one or more conditions for a patientassociated with medical imaging data) can be improved. Furthermore,accuracy and/or efficiency for classification and/or analysis of imagedata (e.g., medical imaging data) can be improved. Additionally,effectiveness of a machine learning model for classification and/oranalysis of image data (e.g., medical imaging data) can be improved,performance of one or more processors that execute a machine learningmodel for classification and/or analysis of image data (e.g., medicalimaging data) can be improved, and/or efficiency of one or moreprocessors that execute a machine learning model for classificationand/or analysis of image data (e.g., medical imaging data) can beimproved.

Referring initially to FIG. 1, there is illustrated an example system100 that provides concurrent image and corresponding multi-channelauxiliary data generation for a generative model, according to one ormore embodiments of the subject disclosure. The system 100 can beemployed by various systems, such as, but not limited to medical devicesystems, medical imaging systems, medical diagnostic systems, medicalsystems, medical modeling systems, enterprise imaging solution systems,advanced diagnostic tool systems, simulation systems, image managementplatform systems, care delivery management systems, artificialintelligence systems, machine learning systems, neural network systems,modeling systems, aviation systems, power systems, distributed powersystems, energy management systems, thermal management systems,transportation systems, oil and gas systems, mechanical systems, machinesystems, device systems, cloud-based systems, heating systems, HVACsystems, medical systems, automobile systems, aircraft systems, watercraft systems, water filtration systems, cooling systems, pump systems,engine systems, prognostics systems, machine design systems, and thelike. In one example, the system 100 can be associated with aclassification system to facilitate visualization and/or interpretationof medical imaging data. Moreover, the system 100 and/or the componentsof the system 100 can be employed to use hardware and/or software tosolve problems that are highly technical in nature (e.g., related toprocessing digital data, related to processing imaging data, related tomedical modeling, related to medical imaging, related to artificialintelligence, etc.), that are not abstract and that cannot be performedas a set of mental acts by a human.

The system 100 can include a generative modeling component 102 that caninclude a multi-channel generator component 104, a discriminatorcomponent 106, and a training component 108. In an embodiment, thegenerative modeling component 102 can be employed for a training phase.For example, the generative modeling component 102 can be employed formulti-channel generative adversarial network training. Aspects of thesystems, apparatuses or processes explained in this disclosure canconstitute machine-executable component(s) embodied within machine(s),e.g., embodied in one or more computer readable mediums (or media)associated with one or more machines. Such component(s), when executedby the one or more machines, e.g., computer(s), computing device(s),virtual machine(s), etc. can cause the machine(s) to perform theoperations described. The system 100 (e.g., the generative modelingcomponent 102) can include memory 112 for storing computer executablecomponents and instructions. The system 100 (e.g., the generativemodeling component 102) can further include a processor 110 tofacilitate operation of the instructions (e.g., computer executablecomponents and instructions) by the system 100 (e.g., the generativemodeling component 102).

The generative modeling component 102 (e.g., the discriminator component106 of the generative modeling component 102) can receive imaging data114. The imaging data 114 can be two-dimensional imaging data and/orthree-dimensional imaging data generated by one or more imaging devices.For instance, the imaging data 114 can be imagery captured via a set ofsensors (e.g., a set of sensors associated with an imaging device). Incertain embodiments, the imaging data 114 can be a series of imagerycaptured via a set of sensors (e.g., a set of sensors associated with animaging device) during an interval of time. The imaging data 114 can bereceived directly from one or more imaging devices. Alternatively, theimaging data 114 can be stored in one or more databases that receivesand/or stores the imaging data 114 associated with the one or moreimaging devices. In certain embodiments, the imaging data 114 can bemedical imaging data. For example, the imaging data 114 can betwo-dimensional medical imaging data and/or three-dimensional medicalimaging data generated by one or more medical imaging devices. Inexample, the imaging data 114 can be electromagnetic radiation imagerycaptured via a set of sensors (e.g., a set of sensors associated with amedical imaging device). In certain embodiments, the imaging data 114can be a series of electromagnetic radiation imagery captured via a setof sensors (e.g., a set of sensors associated with a medical imagingdevice) during an interval of time. In another example, the imaging data114 can be positron emission tomography (PET) scan imagery. In yetanother example, the imaging data 114 can be magnetic resonance imaging(MRI) data. The imaging data 114 can be received directly from one ormore medical imaging devices. Alternatively, the imaging data 114 can bestored in one or more databases that receives and/or stores the medicalimaging data associated with the one or more medical imaging devices. Amedical imaging device can be, for example, an x-ray device, a computedtomography (CT) device, a PET scanner device, an MRI device, anothertype of medical imaging device, etc.

In an embodiment, the imaging data 114 can be multi-channel imagingdata. For example, the imaging data 114 can be a multi-channel datasample of a real image. In an aspect, the imaging data 114 can include areal version of imaging data. Additionally, the imaging data 114 caninclude one or more segmentations of the real version of the imagingdata. For example, in an embodiment, the imaging data 114 can include afirst data channel associated with a real image, a second data channelassociated with first segmentation data indicative of a segmentation forthe real image, and a third data channel associated with secondsegmentation data indicative of a remaining segmentation for the realimage. In certain embodiments, the imaging data 114 can include maskdata. For instance, the imaging data 114 can include one or more masksfor the one or more segmentations of the real version of the imagingdata. In an example, the imaging data 114 can include first mask dataindicative of a first mask (e.g., a first ground truth mask) for thefirst segmentation data and second mask data indicative of a second mask(e.g., a second ground truth mask) for the second segmentation data. Amask included in the imaging data 114 can be a filter to mask one ormore regions in the real version of the imaging data. For instance, amask included in the imaging data 114 can include one or more weightsfor one or more regions of interest in the real version of the imagingdata. In one example, a mask can include a set of pixels that define alocation for a segmentation using binary filtering. Additionally oralternatively, the generative modeling component 102 (e.g., themulti-channel generator component 104 of the generative modelingcomponent 102) can receive one or more latent random variables 116. Theone or more latent random variables 116 can be sampled, for example,from a data distribution of random variables. For instance, the one ormore latent random variables 116 can be a vector of random variables.

The multi-channel generator component 104 can generate syntheticmulti-channel data based on the one or more latent random variables 116.The synthetic multi-channel data can be a multi-channel data sample. Inan aspect, the synthetic multi-channel data can include a syntheticversion of imaging data. Additionally, the synthetic multi-channel datacan include one or more segmentations of the synthetic version of theimaging data. The synthetic version of the imaging data and the one ormore segmentations can be associated with unique data channels. Forexample, in an embodiment, the multi-channel generator component 104 cangenerate a first data channel associated with a synthetic image, asecond data channel associated with first segmentation data indicativeof a segmentation for the synthetic image, and a third data channelassociated with second segmentation data indicative of a remainingsegmentation for the synthetic image. In another embodiment, themulti-channel generator component 104 can generate a first data channelassociated with a synthetic image, a second data channel associated withfirst segmentation data indicative of a first segmentation for thesynthetic image, a third data channel associated with secondsegmentation data indicative of a remaining portion of the firstsegmentation for the synthetic image, a fourth data channel associatedwith third segmentation data indicative of a second segmentation for thesynthetic image, and a fifth data channel associated with fourthsegmentation data indicative of a remaining portion of the secondsegmentation for the synthetic image. However, it is to be appreciatedthat the synthetic multi-channel data generated by the multi-channelgenerator component 104 can be associated with a different number ofdata channels and/or a different number of segmentations. In anembodiment, the synthetic multi-channel data generated by themulti-channel generator component 104 can include mask data. Forinstance, the multi-channel generator component 104 can generate one ormore masks for the one or more segmentations of the synthetic version ofthe imaging data. In an example, the multi-channel generator component104 can generate first mask data indicative of a first mask (e.g., afirst ground truth mask) for the first segmentation data, themulti-channel generator component 104 can generate second mask dataindicative of a second mask (e.g., a second ground truth mask) for thesecond segmentation data, etc. A mask included in the syntheticmulti-channel data can be a filter to mask one or more regions in thesynthetic version of the imaging data. For instance, a mask included inthe synthetic multi-channel data can include one or more weights for oneor more regions of interest in the synthetic version of the imagingdata. In one example, a mask can include a set of pixels that define alocation for a segmentation using binary filtering.

In another embodiment, the multi-channel generator component 104 canemploy a deep neural network such as, for example, a convolutionalneural network to generate the synthetic multi-channel data. In certainembodiments, the convolutional neural network can be a spring network ofconvolutional layers. For instance, the convolutional neural network canperform a plurality of sequential and/or parallel downsampling and/orupsampling of the one or more latent random variables 116 associatedwith convolutional layers of the convolutional neural network. In anexample, the convolutional neural network can perform a firstconvolutional layer process associated with sequential downsampling ofthe one or more latent random variables 116 and a second convolutionallayer process associated with sequential upsampling of the one or morelatent random variables 116. The spring network of convolutional layerscan include the first convolutional layer process associated with thesequential downsampling and the second convolutional layer processassociated with sequential upsampling. The spring network ofconvolutional layers associated with the convolutional neural networkcan alter convolutional layer filters similar to functionality of aspring. For instance, the convolutional neural network can analyze theone or more latent random variables 116 based on a first convolutionallayer filter that comprises a first size, a second convolutional layerfilter that comprises a second size that is different than the firstsize, and a third convolutional layer filter that comprises the firstsize associated with the first convolutional layer filter.

The discriminator component 106 can predict the imaging data 114 with afirst predicted class set or a second predicted class set. Additionallyor alternatively, the discriminator component 106 can predict thesynthetic multi-channel data with the first predicted class set or thesecond predicted class set. In an embodiment, a class set can include amulti-channel image (e.g., channel level labels), an image label (e.g.,image level labels) and/or clinical labels. The first predicted classset can be associated with a first classification (e.g., a firstclassification label) and the second predicted class set can beassociated with a second classification (e.g., a second classificationlabel). For example, an image level label can correspond to a “real”label for an image (e.g., a “real” classification label) or a “fake”label for an image (e.g., a “fake” classification label). In anotherexample, a channel level label can correspond to a “real” label for amulti-channel image (e.g., a “real” classification label) or a “fake”label for a multi-channel image (e.g., a “fake” classification label).In an embodiment, the discriminator component 106 can label thesynthetic multi-channel data with a first label (e.g., a “real” label)or a second label (e.g., a “fake” label) based on the first data channelassociated with the synthetic image, the second data channel associatedwith the first segmentation data, and the third data channel associatedwith the second segmentation data. In another embodiment, thediscriminator component 106 can label the synthetic multi-channel datawith a first label (e.g., a “real” label) or a second label (e.g., a“fake” label) based on the first data channel associated with thesynthetic image, the second data channel associated with the firstsegmentation data, the third data channel associated with the secondsegmentation data, the fourth data channel associated with the thirdsegmentation data, and the fifth data channel associated with the fourthsegmentation data.

In certain embodiments, the discriminator component 106 can employ adeep neural network such as, for example, a convolutional neural networkto label (e.g., classify) the imaging data 114 and/or the syntheticmulti-channel data. In certain embodiments, the convolutional neuralnetwork can be a spring network of convolutional layers. For instance,the convolutional neural network can perform a plurality of sequentialand/or parallel downsampling and/or upsampling of the imaging data 114and/or the synthetic multi-channel data associated with convolutionallayers of the convolutional neural network. In an example, theconvolutional neural network can perform a first convolutional layerprocess associated with sequential downsampling of the imaging data 114and/or the synthetic multi-channel data and a second convolutional layerprocess associated with sequential upsampling of the imaging data 114and/or the synthetic multi-channel data. The spring network ofconvolutional layers can include the first convolutional layer processassociated with the sequential downsampling and the second convolutionallayer process associated with sequential upsampling. The spring networkof convolutional layers associated with the convolutional neural networkcan alter convolutional layer filters similar to functionality of aspring. For instance, the convolutional neural network can analyze theimaging data 114 and/or the synthetic multi-channel data based on afirst convolutional layer filter that comprises a first size, a secondconvolutional layer filter that comprises a second size that isdifferent than the first size, and a third convolutional layer filterthat comprises the first size associated with the first convolutionallayer filter. In certain embodiments, the discriminator component 106can extract information that is indicative of correlations, inferencesand/or expressions from the imaging data 114 and/or the multi-channelimaging data based on the convolutional neural network. In an aspect,the discriminator component 106 can perform learning with respect to theimaging data 114 and/or the multi-channel imaging data explicitly orimplicitly using the convolutional neural network. The discriminatorcomponent 106 can also employ an automatic classification system and/oran automatic classification process to facilitate analysis of theimaging data 114 and/or the multi-channel imaging data. For example, thediscriminator component 106 can employ a probabilistic and/orstatistical-based analysis (e.g., factoring into the analysis utilitiesand costs) to learn and/or generate inferences with respect to theimaging data 114 and/or the multi-channel imaging data. Thediscriminator component 106 can employ, for example, a support vectormachine (SVM) classifier to learn and/or generate inferences for theimaging data 114 and/or the multi-channel imaging data. Additionally oralternatively, the discriminator component 106 can employ otherclassification techniques associated with Bayesian networks, decisiontrees and/or probabilistic classification models. Classifiers employedby the discriminator component 106 can be explicitly trained (e.g., viaa generic training data) as well as implicitly trained (e.g., viareceiving extrinsic information). For example, with respect to SVM's,SVM's can be configured via a learning or training phase within aclassifier constructor and feature selection module. A classifier can bea function that maps an input attribute vector, x=(x1, x2, x3, x4, xn),to a confidence that the input belongs to a class—that is,f(x)=confidence(class).

The training component 108 can employ the first predicted label or thesecond predicted label for the imaging data 114 to train a generativeartificial intelligence model. Additionally or alternatively, thetraining component 108 can employ the first predicted label or thesecond predicted label for the synthetic multi-channel data to train thegenerative artificial intelligence model. The generative artificialintelligence model can be a model for a generative network. For example,the generative artificial intelligence model can be a model for agenerative adversarial network. The generative network (e.g., thegenerative adversarial network) can be employed to generate imagesand/or corresponding annotations for the images. For example, thegenerative network (e.g., the generative adversarial network) can beemployed to generate medical images and/or corresponding annotations forthe medical images. In an embodiment, the training component 108 cangenerate and/or utilize a loss function to tune the generativeartificial intelligence model based on the first predicted label or thesecond predicted label for the imaging data 114. For example, thetraining component 108 can generate and/or utilize a loss function totune a discriminator of a generative adversarial network (e.g., to tunea convolutional neural network for a discriminator of a generativeadversarial network) based on the first predicted label or the secondpredicted label for the imaging data 114. The loss function associatedwith the imaging data 114 can be, for example, a cross-entropy errorfunction. Furthermore, the generative adversarial network can bemodified based on the loss function associated with the imaging data 114to improve classification output from the generative adversarialnetwork. Additionally or alternatively, the training component 108 cangenerate and/or utilize a loss function to tune the generativeartificial intelligence model based on the first predicted label or thesecond predicted label for the multi-channel imaging data. For example,the training component 108 can generate and/or utilize a loss functionto tune a multi-channel generator of a generative adversarial network(e.g., to tune a convolutional neural network for a multi-channelgenerator of a generative adversarial network) based on the firstpredicted label or the second predicted label for the syntheticmulti-channel data. The loss function associated with the syntheticmulti-channel data can be, for example, a cross-entropy error function.Furthermore, the generative adversarial network can be modified based onthe loss function associated with the synthetic multi-channel data toimprove generation of an image from a data distribution associated withthe generative adversarial network. In certain embodiments, a lossfunction generated by the training component 108 can include a set ofweights to tune one or more parameters of the generative artificialintelligence model. In an embodiment, the generative modeling component102 can generate a generative model 118. For example, the generativemodel 118 can be the generative artificial intelligence model trained bythe training component 108. In certain embodiments, the generative model118 can be associated with the loss function related to the imaging data114 and/or the loss function related to the synthetic multi-channeldata.

It is to be appreciated that technical features of the generativemodeling component 102 are highly technical in nature and not abstractideas. Processing threads of the generative modeling component 102 thatprocess and/or analyze the imaging data 114 and/or the one or morelatent random variables 116, etc. cannot be performed by a human (e.g.,are greater than the capability of a single human mind). For example,the amount of the imaging data 114 processed, the speed of processing ofthe imaging data 114 and/or the data types of the imaging data 114processed by the generative modeling component 102 over a certain periodof time can be respectively greater, faster and different than theamount, speed and data type that can be processed by a single human mindover the same period of time. Furthermore, the imaging data 114processed by the generative modeling component 102 can be one or moremedical images generated by sensors of a medical imaging device.Moreover, the generative modeling component 102 can be fully operationaltowards performing one or more other functions (e.g., fully powered on,fully executed, etc.) while also processing the imaging data 114.

Referring now to FIG. 2, there is illustrated a non-limitingimplementation of a system 200 in accordance with various aspects andimplementations of this disclosure. Repetitive description of likeelements employed in other embodiments described herein is omitted forsake of brevity. The system 200 includes the generative modelingcomponent 102. The generative modeling component 102 can include themulti-channel generator component 104, the discriminator component 106,the training component 108, the processor 110 and/or the memory 112. Inthe embodiment shown in FIG. 2, the multi-channel generator component104 can include a synthetic-image generator component 202. Thesynthetic-image generator component 202 can generate one or moresynthetic images based on the one or more latent random variables 116.In an aspect, the synthetic-image generator component 202 can generate asynthetic image for a data channel included in the syntheticmulti-channel data. For example, the synthetic-image generator component202 can generate a synthetic image for a first data channel of thesynthetic multi-channel data. Furthermore, at least a second datachannel of the synthetic multi-channel data can include a segmentationof the synthetic image generated by the synthetic-image generatorcomponent 202. In an embodiment, the synthetic-image generator component202 can generate one or more synthetic images based on a datadistribution (e.g., a probability distribution) associated with the oneor more latent random variables 116. For instance, the synthetic-imagegenerator component 202 can generate one or more synthetic images basedon a vector of data associated with the one or more latent randomvariables 116. In another embodiment, the synthetic-image generatorcomponent 202 can generate one or more synthetic images based on arandom data distribution associated with the one or more latent randomvariables 116. For example, in an embodiment, the synthetic-imagegenerator component 202 can generate one or more synthetic images basedon a random vector of data (e.g., a vector of random data values)associated with the one or more latent random variables 116. In yetanother embodiment, the synthetic-image generator component 202 cangenerate one or more synthetic images based on a set of latent randomvariables associated with the one or more latent random variables 116.

Referring now to FIG. 3, there is illustrated a non-limitingimplementation of a system 300 in accordance with various aspects andimplementations of this disclosure. Repetitive description of likeelements employed in other embodiments described herein is omitted forsake of brevity. The system 300 includes the generative modelingcomponent 102. The generative modeling component 102 can include themulti-channel generator component 104, the discriminator component 106,the training component 108, the processor 110 and/or the memory 112. Inthe embodiment shown in FIG. 3, the multi-channel generator component104 can include the synthetic-image generator component 202 and/or asegmentation component 302. The segmentation component 302 can generateone or more segmentations associated with the one or more syntheticimages generated by the synthetic-image generator component 202. In anaspect, the segmentation component 302 can generate segmentation datafor one or more data channels included in the synthetic multi-channeldata. For example, the segmentation component 302 can generate firstsegmentation data indicative of a segmentation for the synthetic imagegenerated by the synthetic-image generator component 202. The firstsegmentation data can be included in a data channel (e.g., a second datachannel) of the synthetic multi-channel data. Additionally, thesegmentation component 302 can generate second segmentation dataindicative of a remaining segmentation for the synthetic image generatedby the synthetic-image generator component 202. The second segmentationdata can be included in a different data channel (e.g., a third datachannel) of the synthetic multi-channel data. A segmentation generatedby the segmentation component 302 can correspond to an annotation forthe synthetic image generated by the synthetic-image generator component202.

In another embodiment, the segmentation component 302 can additionallyor alternatively generate one or more segmentations associated with theimaging data 114. For example, the segmentation component 302 canadditionally or alternatively generate one or more segmentations for oneor more real images included in the imaging data 114. In an aspect, thesegmentation component 302 can generate segmentation data for one ormore data channels included in the imaging data 114. For example, thesegmentation component 302 can generate first segmentation dataindicative of a segmentation for a real image included in the imagingdata 114 (e.g., a real image included in a first data channel of theimaging data 114). The first segmentation data can be included in a datachannel (e.g., a second data channel) of the imaging data 114.Additionally, the segmentation component 302 can generate secondsegmentation data indicative of a remaining segmentation for the realimage included in the imaging data 114. The second segmentation data canbe included in a different data channel (e.g., a third data channel) ofthe imaging data 114. A segmentation generated by the segmentationcomponent 302 can additionally or alternatively correspond to anannotation for the real image included in the imaging data 114.

Referring now to FIG. 4, there is illustrated a non-limitingimplementation of a system 400 in accordance with various aspects andimplementations of this disclosure. Repetitive description of likeelements employed in other embodiments described herein is omitted forsake of brevity. The system 400 includes the generative modelingcomponent 102 and a pre-processing component 402. The generativemodeling component 102 can include the multi-channel generator component104, the discriminator component 106, the training component 108, theprocessor 110 and/or the memory 112. In the embodiment shown in FIG. 4,the multi-channel generator component 104 can include thesynthetic-image generator component 202 and/or the segmentationcomponent 302. In certain embodiments, the generative modeling component102 (e.g., the processor 110 of the generative modeling component 102)can generate the one or more latent random variables 116. Thepre-processing component 402 can receive the imaging data 114 and/ormask data 404. Based on the imaging data 114 and/or the mask data 404,the pre-processing component 402 can prepare multi-channel data 406. Themulti-channel data 406 can be provided to the generative modelingcomponent 102. The mask data 404 can include one or more ground truthmasks for one or more images included in the imaging data 114. In anembodiment, the mask data 404 can be a set of masks from a plurality ofobjects. For example, each medical image from the imaging data 114 canbe associated with one or more masks. A mask included in the mask data404 can be a filter to mask one or more regions in an image (e.g., inthe imaging data 114). For instance, a mask included in the mask data404 can include one or more weights for one or more regions of interestin an image (e.g., in the imaging data 114). In one example, a maskincluded in the mask data 404 can include a set of pixels that define alocation for region of interests using binary filtering. Themulti-channel data 406 can be multi-channel imaging data. For example,the multi-channel data 406 can be a multi-channel data sample of a realimage included in the imaging data 114. The multi-channel data 406 canadditionally or alternatively include one or more segmentations of thereal image included in the imaging data 114. For example, in anembodiment, the multi-channel data 406 can include a first data channelassociated with a real image, a second data channel associated withfirst segmentation data indicative of a segmentation for the real image,and a third data channel associated with second segmentation dataindicative of a remaining segmentation for the real image.

Referring now to FIG. 5, there is illustrated a non-limitingimplementation of a system 500 in accordance with various aspects andimplementations of this disclosure. Repetitive description of likeelements employed in other embodiments described herein is omitted forsake of brevity. The system 500 includes an inferencing component 102′and medical imaging diagnosis process 502. The inferencing component102′ can be an alternate embodiment of the generative modeling component102. For example, the inferencing component 102′ can be employed for aninferencing phase and/or can be employed as a generative modeling basedinferencing component. The inferencing component 102′ can include themulti-channel generator component 104, the processor 110 and/or thememory 112. In certain embodiments, the inferencing component 102′ canadditionally include the discriminator component 106. In the embodimentshown in FIG. 5, the multi-channel generator component 104 can includethe synthetic-image generator component 202, the segmentation component302 and/or a post-processing component 503. In certain embodiments, themedical imaging diagnosis process 502 can be a component (e.g., amedical imaging diagnosis component) included in the inferencingcomponent 102′. The generative modeling 102 can provide syntheticmulti-channel data 506 and/or other information to the medical imagingdiagnosis process 502.

The post-processing component 503 can generate mask data associated withthe segmentation data generated by the segmentation component 302. Forexample, the post-processing component 503 can generate one or moremasks for the one or more segmentations generated by the segmentationcomponent 302. A mask generated by the post-processing component 503 canbe a binary mask that corresponds to an annotation associated with asegmentation. In an embodiment, the post-processing component 503 cangenerate first mask data indicative of a first mask (e.g., a firstground truth mask) for the first segmentation data indicative of thesegmentation for the synthetic image generated by the synthetic-imagegenerator component 202. Additionally, the post-processing component 503can generate second mask data indicative of a second mask (e.g., asecond ground truth mask) for the second segmentation data indicative ofthe remaining segmentation for the synthetic image generated by thesynthetic-image generator component 202. A mask generated by thepost-processing component 503 for the synthetic multi-channel data canbe a filter to mask one or more regions in the synthetic image generatedby the synthetic-image generator component 202. For instance, a maskincluded in the synthetic image generated by the synthetic-imagegenerator component 202 can include a binary weight for a segmentationin the synthetic image generated by the synthetic-image generatorcomponent 202. In one example, a mask included in the synthetic imagegenerated by the synthetic-image generator component 202 can include aset of pixels that define a location for a segmentation for thesynthetic image using binary filtering.

In another embodiment, the post-processing component 503 canadditionally or alternatively generate first mask data indicative of afirst mask (e.g., a first ground truth mask) for the first segmentationdata indicative of the segmentation for the real image included in theimaging data 114. Additionally, the post-processing component 503 cangenerate second mask data indicative of a second mask (e.g., a secondground truth mask) for the second segmentation data indicative of theremaining segmentation for the real image included in the imaging data114. A mask included in the imaging data 114 can be a filter to mask oneor more regions in the real image included in the imaging data 114. Forinstance, a mask included in the imaging data 114 can include a binaryweight for a segmentation in the real image included in the imaging data114. In one example, a mask included in the imaging data 114 can includea set of pixels that define a location for a segmentation associatedwith the real image using binary filtering. Mask data and/or otherinformation generated by the multi-channel generator component 104(e.g., the synthetic-image generator component 202, the segmentationcomponent 302 and/or the post-processing component 503) can be includedin the synthetic multi-channel data 506. For instance, the syntheticmulti-channel data 506 can be a multi-channel data sample. In an aspect,the synthetic multi-channel data 506 can include a synthetic version ofimaging data (e.g., an image). Additionally, the synthetic multi-channeldata 506 can include one or more segmentations of the synthetic versionof the imaging data. The synthetic version of the imaging data and theone or more segmentations can be associated with unique data channels.For example, in an embodiment, the synthetic multi-channel data 506 caninclude a first data channel associated with a synthetic image, a seconddata channel associated with first segmentation data indicative of asegmentation for the synthetic image, and a third data channelassociated with second segmentation data indicative of a remainingsegmentation for the synthetic image.

The medical imaging diagnosis process 502 can employ the syntheticmulti-channel data 506 and/or other information provided by thegenerative modeling component 102 to facilitate classification,localization, detection and/or segmentation of one or more featuresassociated with an input image. In an embodiment, the medical imagingdiagnosis process 502 can perform deep learning to facilitateclassification, localization, detection and/or segmentation of one ormore diseases associated with an input image (e.g., a medical image). Inanother embodiment, the medical imaging diagnosis process 502 canperform deep learning based on a convolutional neural network thatreceives an input image (e.g., a medical image). A disease classifiedand/or localized by the medical imaging diagnosis process 502 caninclude, for example, a lung disease, a heart disease, a tissue disease,a bone disease, a tumor, a cancer, tuberculosis, cardiomegaly,hypoinflation of a lung, opacity of a lung, hyperdistension, a spinedegenerative disease, calcinosis, or another type of disease associatedwith an anatomical region of a patient body. In an aspect, the medicalimaging diagnosis process 502 can determine a prediction for a diseaseassociated with an input image (e.g., a medical image). For instance,the medical imaging diagnosis process 502 can determine a probabilityscore for a disease associated with an input image (e.g., a medicalimage). In an example, the medical imaging diagnosis process 502 candetermine a first percentage value representing likelihood of a negativeprognosis for the disease and a second value representing a likelihoodof a positive prognosis for the disease.

In certain embodiments, the medical imaging diagnosis process 502 cangenerate a multi-dimensional visualization associated withclassification and/or localization of one or more diseases associatedwith an input image (e.g., a medical image). For instance, the medicalimaging diagnosis process 502 can generate a human-interpretablevisualization associated with classification and/or localization of oneor more diseases associated with an input image (e.g., a medical image).Additionally or alternatively, the medical imaging diagnosis process 502can generate a human-interpretable visualization of an input image(e.g., a medical image). In an embodiment, the medical imaging diagnosisprocess 502 can generate deep learning data based on a classificationand/or a localization for a portion of an anatomical region associatedwith the input image. The deep learning data can include, for example, aclassification and/or a location for one or more diseases located in theinput image. In certain embodiments, the deep learning data can includeprobability data indicative of a probability for one or more diseasesbeing located in the input image. The probability data can be, forexample, a probability array of data values for one or more diseasesbeing located in an input image (e.g., a medical image). Themulti-dimensional visualization can be a graphical representation of aninput image (e.g., a medical image) that shows a classification and/or alocation of one or more diseases with respect to a patient body. Themedical imaging diagnosis process 502 can also generate a display of themulti-dimensional visualization of the diagnosis provided by a medicalimaging diagnosis process 502. For example, the medical imagingdiagnosis process 502 can render a 2D visualization of a portion of ananatomical region on a user interface associated with a display of auser device such as, but not limited to, a computing device, a computer,a desktop computer, a laptop computer, a monitor device, a smart device,a smart phone, a mobile device, a handheld device, a tablet, a wearablecomputing device, a portable computing device or another type of userdevice associated with a display. In an aspect, the multi-dimensionalvisualization can include deep learning data. In another aspect, thedeep learning data can also be rendered on the 3D model as one or moredynamic visual elements. The medical imaging diagnosis process 502 can,in an embodiment, alter visual characteristics (e.g., color, size, hues,shading, etc.) of at least a portion of the deep learning dataassociated with the multi-dimensional visualization based on theclassification and/or the localization for the portion of the anatomicalregion. For example, the classification and/or the localization for theportion of the anatomical region can be presented as different visualcharacteristics (e.g., colors, sizes, hues or shades, etc.), based on aresult of deep learning and/or the medical imaging diagnosis process502. In another aspect, the medical imaging diagnosis process 502 canallow a user to zoom into or out with respect to the deep learning dataassociated with the multi-dimensional visualization. For example, themedical imaging diagnosis process 502 can allow a user to zoom into orout with respect to a classification and/or a location of one or morediseases identified in the anatomical region of the patient body. Assuch, a user can view, analyze and/or interact with the deep learningdata associated with the multi-dimensional visualization for an inputimage (e.g., a medical image).

Referring now to FIG. 6, there is illustrated a non-limitingimplementation of a system 600 in accordance with various aspects andimplementations of this disclosure. Repetitive description of likeelements employed in other embodiments described herein is omitted forsake of brevity. The system 600 can represent a generative network. Forexample, the system 600 can represent a generative adversarial network.The system 600 can include a multi-channel generator 602 and adiscriminator 604. The multi-channel generator 602 can be associatedwith the multi-channel generator component 104. The discriminator 604can be associated with the discriminator component 106. Themulti-channel generator 602 can be a convolutional neural network thatgenerates a multi-channel data sample 612 based on a latent randomvariable 610. The multi-channel data sample 612 can be, for example, thesynthetic multi-channel data generated by the multi-channel generatorcomponent 104. The latent random variable 610 can be included in the oneor more latent random variables 116, for example. In one example, thelatent random variable 610 can be a latent code that facilitatesgeneration of a unique synthetic image. The discriminator 604 can be aconvolutional neural network that classifies a multi-channel data sample608 from the multi-channel data sample 612. For example, thediscriminator 604 can generate a classification label 614 thatcorresponds to classification for the multi-channel data sample 608 fromthe multi-channel data sample 612. The classification label 614 can be afirst classification label (e.g., a “real” classification label) or asecond classification label (e.g., a “fake” classification label) forthe multi-channel data sample 608 and/or the multi-channel data sample612. The multi-channel data sample 608 can be generated based onmulti-channel real data 606. The multi-channel real data 606 and/or themulti-channel data sample 608 can correspond to the imaging data 114.

In an embodiment, a loss function 616 can be generated based on theclassification label 614. The loss function 616 can be one or more lossfunctions generated by the training component 108. In an embodiment, themulti-channel real data 606 can be a real image (e.g., a real medicalimage). In certain embodiments, pre-processing 605 can be performed toprepare the multi-channel real data 606. For instance, thepre-processing 605 can prepare the multi-channel real data 606 based onimaging data 601 and/or mask data 603. The mask data 603 can include oneor more corresponding masks for one or more images included in theimaging data 601. Furthermore, the multi-channel data sample 608 caninclude multiple data channels. For instance, the multi-channel datasample 608 can include a data channel that includes the real image(e.g., the real medical image). The multi-channel data sample 608 canalso include one or more data channels that include a segmentation forthe real image. In certain embodiments, the multi-channel data sample608 can also include one or more data channels that include a mask for asegmentation for the real image. In another embodiment, themulti-channel data sample 612 can include multiple data channels. Forinstance, the multi-channel data sample 612 can include a data channelthat includes the synthetic image (e.g., the synthetic medical image).The multi-channel data sample 612 can also include one or more datachannels that include a segmentation for the synthetic image. In certainembodiments, the multi-channel data sample 612 can also include one ormore data channels that include a mask for a segmentation for thesynthetic image. In certain embodiments, the loss function 616 can tunethe multi-channel generator 602 and/or the discriminator 604. In oneexample, the loss function 616 can be an error gradient to modify one ormore parameters for the multi-channel generator 602 and/or thediscriminator 604. For instance, the loss function 616 can be generatedbased on the multi-channel data sample 608 and/or the multi-channel datasample 612 to tune the generative network. In certain embodiments, thediscriminator 604 can be discarded from the generative network inresponse to a determination that the multi-channel generator 602 istrained. For example, in an embodiment associated with an inferencingphase, the discriminator 604 can be discarded from the generativenetwork in response to a determination that the multi-channel generator602 is trained

Referring now to FIG. 7, there is illustrated a non-limitingimplementation of a system 700 in accordance with various aspects andimplementations of this disclosure. Repetitive description of likeelements employed in other embodiments described herein is omitted forsake of brevity. The system 700 can be employed, for example, during aninferencing phase. The system 700 can include the multi-channelgenerator 602. In an embodiment, the multi-channel generator 602 cangenerate a synthetic image 702, a segmentation 704 and a segmentation706. For instance, the multi-channel generator component 104 cangenerate the synthetic image 702, the segmentation 704 and thesegmentation 706. The multi-channel generator 602 can, for example,generate the synthetic image 702 based on the latent random variable 610and/or other data (e.g., the one or more latent random variables 116).The synthetic image 702 can be, for example, a synthetic medical image(e.g., a synthetic x-ray image, etc.). The segmentation 704 can be asegmentation of the synthetic image 702. For example, the segmentation704 can correspond to an annotation of the synthetic image 702. Thesegmentation 706 can be a remaining segmentation of the synthetic image702. For example, the segmentation 706 can be a complementarysegmentation as compared to the segmentation 704. In an embodiment, thesynthetic image 702 can be included in a first data channel of thesynthetic multi-channel data (e.g., the multi-channel data sample 612)generated by the multi-channel generator component 104. The segmentation704 can be included in a second data channel of the syntheticmulti-channel data (e.g., the multi-channel data sample 612) generatedby the multi-channel generator component 104. Furthermore, thesegmentation 706 can be included in a third data channel of thesynthetic multi-channel data (e.g., the multi-channel data sample 612)generated by the multi-channel generator component 104. As such, aconcurrent image and corresponding multi-channel auxiliary data can beprovided without intervention by a user to annotate at pixel level on asynthetic image. It is to be appreciated that, in certain embodiments,the multi-channel generator 602 can generate more than two segmentations(e.g., more than three data channels can be generated).

Referring now to FIG. 8, there is illustrated a non-limitingimplementation of a system 800 in accordance with various aspects andimplementations of this disclosure. Repetitive description of likeelements employed in other embodiments described herein is omitted forsake of brevity. The system 800 can be employed, for example, during aninferencing phase (e.g., for binary mask generation). The system 800 caninclude post-processing 802. The post-processing 802 can be associatedwith the post-processing component 503. In an embodiment, thepost-processing 802 can perform processing to generate a mask 804 basedon the segmentation 704. Furthermore, the post-processing 802 canperform processing to generate a mask 806 based on the segmentation 706.The mask 806 can be an opposite mask as compared to the mask 804. Forexample, the mask 806 can be a complementary mask as compared to themask 804. The mask 804 and/or the mask 806 can be employed, for example,for respective model training data. The mask 804 can be a binary maskthat corresponds to an annotation associated with the segmentation 704.In one example, the mask 804 can be a ground truth mask that correspondsto an annotation associated with the segmentation 704. The mask 806 canbe a binary mask that corresponds to an annotation associated with thesegmentation 706. In one example, the mask 806 can be a ground truthmask that corresponds to an annotation associated with the segmentation706. In an embodiment, the mask 804 can be included in a second datachannel of the synthetic multi-channel data (e.g., the multi-channeldata sample 612) generated by the multi-channel generator component 104.Furthermore, the mask 806 can be included in a third data channel of thesynthetic multi-channel data (e.g., the multi-channel data sample 612)generated by the multi-channel generator component 104.

FIG. 9 illustrates a methodology and/or a flow diagram in accordancewith the disclosed subject matter. For simplicity of explanation, themethodology is depicted and described as a series of acts. It is to beunderstood and appreciated that the subject innovation is not limited bythe acts illustrated and/or by the order of acts, for example acts canoccur in various orders and/or concurrently, and with other acts notpresented and described herein. Furthermore, not all illustrated actsmay be required to implement the methodology in accordance with thedisclosed subject matter. In addition, those skilled in the art willunderstand and appreciate that the methodology could alternatively berepresented as a series of interrelated states via a state diagram orevents. Additionally, it should be further appreciated thatmethodologies disclosed hereinafter and throughout this specificationare capable of being stored on an article of manufacture to facilitatetransporting and transferring such methodologies to computers. The termarticle of manufacture, as used herein, is intended to encompass acomputer program accessible from any computer-readable device or storagemedia.

Referring to FIG. 9, there is illustrated a non-limiting implementationof a methodology 900 for providing concurrent image and correspondingmulti-channel auxiliary data generation for a generative model,according to an aspect of the subject innovation. At 902, syntheticmulti-channel data associated with a synthetic version of imaging datais generated, by a system operatively coupled to a processor (e.g., bymulti-channel generator component 104). The synthetic multi-channel datacan be a multi-channel data sample. In an aspect, the syntheticmulti-channel data can include a synthetic version of imaging data.Additionally, the synthetic multi-channel data can include one or moresegmentations of the synthetic version of the imaging data. Thesynthetic version of the imaging data and the one or more segmentationscan be associated with unique data channels. For example, in anembodiment, the synthetic multi-channel can include a first data channelassociated with a synthetic image, a second data channel associated withfirst segmentation data indicative of a segmentation for the syntheticimage, and a third data channel associated with second segmentation dataindicative of a remaining segmentation for the synthetic image. Inanother embodiment, the synthetic multi-channel can include a first datachannel associated with a synthetic image, a second data channelassociated with first segmentation data indicative of a firstsegmentation for the synthetic image, a third data channel associatedwith second segmentation data indicative of a remaining portion of thefirst segmentation for the synthetic image, a fourth data channelassociated with third segmentation data indicative of a secondsegmentation for the synthetic image, and a fifth data channelassociated with fourth segmentation data indicative of a remainingportion of the second segmentation for the synthetic image.

At 904, multi-channel imaging data and/or the synthetic multi-channeldata are predicted, by the system (e.g., by discriminator component106), with a first predicted class set or a second predicted class set.In an embodiment, a class set can include a multi-channel image (e.g.,channel level labels), an image label (e.g., image level labels) and/orclinical labels. The first predicted class set can be associated with afirst classification (e.g., a first classification label) and the secondpredicted class set can be associated with a second classification(e.g., a second classification label). For example, an image level labelcan correspond to a “real” label for an image (e.g., a “real”classification label) or a “fake” label for an image (e.g., a “fake”classification label). In another example, a channel level label cancorrespond to a “real” label for a multi-channel image (e.g., a “real”classification label) or a “fake” label for a multi-channel image (e.g.,a “fake” classification label). The multi-channel imaging data can be amulti-channel data sample. In an aspect, the multi-channel imaging datacan include a real version of imaging data. Additionally, themulti-channel imaging data can include one or more segmentations of thereal version of the imaging data. The real version of the imaging dataand the one or more segmentations can be associated with unique datachannels. For example, in an embodiment, the multi-channel imaging datacan include a first data channel associated with a real image, a seconddata channel associated with first segmentation data indicative of asegmentation for the real image, and a third data channel associatedwith second segmentation data indicative of a remaining segmentation forthe real image. In another embodiment, the multi-channel imaging datacan include a first data channel associated with a real image, a seconddata channel associated with first segmentation data indicative of afirst segmentation for the real image, a third data channel associatedwith second segmentation data indicative of a remaining portion of thefirst segmentation for the real image, a fourth data channel associatedwith third segmentation data indicative of a second segmentation for thereal image, and a fifth data channel associated with fourth segmentationdata indicative of a remaining portion of the second segmentation forthe real image. The real image can be a two-dimensional image or athree-dimensional image generated by one or more medical imagingdevices. For instance, the real image can be electromagnetic radiationimagery captured via a set of sensors (e.g., a set of sensors associatedwith a medical imaging device). The real image can be received directlyfrom one or more medical imaging devices. Alternatively, the real imagecan be stored in one or more databases that receives and/or stores thereal image associated with the one or more medical imaging devices. Amedical imaging device can be, for example, an x-ray device, a CTdevice, another type of medical imaging device, etc.

At 906, a generative adversarial network model is trained, by the system(e.g., by training component 108), based on the first predicted classset or the second predicted class set for the multi-channel imaging dataand/or the synthetic multi-channel data. The generative adversarialnetwork model can be, for example, a generative network. The generativeadversarial network model can be employed to generate images and/orcorresponding annotations for the images. For example, the generativeadversarial network model can be employed to generate medical imagesand/or corresponding annotations for the medical images. In anembodiment, a loss function can be generated based on the firstpredicted label or the second predicted label for the for themulti-channel imaging data and/or the synthetic multi-channel data. Theloss function can be employed to tune the generative artificialintelligence model. For example, a multi-channel generator or adiscriminator of the generative artificial intelligence model can betuned based on the first predicted label or the second predicted labelfor the for the multi-channel imaging data and/or the syntheticmulti-channel data. The loss function can be, for example, across-entropy error function. Furthermore, the loss function can beemployed to improve classification output from the generativeadversarial network model. In an embodiment, the loss function can be aloss function associated with loss between first data channel, thesecond data channel and/or the third data channel. In an embodiment, theloss function can be a loss function associated with loss between firstdata channel, the second data channel, the third data channel, thefourth data channel and/or the fifth data channel.

At 908, it is determined whether the generative adversarial networkmodel satisfies a defined criterion. For example, it can be determinedwhether a multi-channel generator and/or a discriminator for thegenerative adversarial network model is adequately tuned. If no, themethodology 900 can return to 902. If yes, the methodology 900 can end.In certain embodiments, the methodology 900 can additionally oralternatively include generating first mask data indicative of a firstground truth mask for the first segmentation data. Furthermore, themethodology 900 can additionally or alternatively include generatingsecond mask data indicative of a second ground truth mask for the secondsegmentation data.

The aforementioned systems and/or devices have been described withrespect to interaction between several components. It should beappreciated that such systems and components can include thosecomponents or sub-components specified therein, some of the specifiedcomponents or sub-components, and/or additional components.Sub-components could also be implemented as components communicativelycoupled to other components rather than included within parentcomponents. Further yet, one or more components and/or sub-componentsmay be combined into a single component providing aggregatefunctionality. The components may also interact with one or more othercomponents not specifically described herein for the sake of brevity,but known by those of skill in the art.

In order to provide a context for the various aspects of the disclosedsubject matter, FIGS. 10 and 11 as well as the following discussion areintended to provide a brief, general description of a suitableenvironment in which the various aspects of the disclosed subject mattermay be implemented.

With reference to FIG. 10, a suitable environment 1000 for implementingvarious aspects of this disclosure includes a computer 1012. Thecomputer 1012 includes a processing unit 1014, a system memory 1016, anda system bus 1018. The system bus 1018 couples system componentsincluding, but not limited to, the system memory 1016 to the processingunit 1014. The processing unit 1014 can be any of various availableprocessors. Dual microprocessors and other multiprocessor architecturesalso can be employed as the processing unit 1014.

The system bus 1018 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI).

The system memory 1016 includes volatile memory 1020 and nonvolatilememory 1022. The basic input/output system (BIOS), containing the basicroutines to transfer information between elements within the computer1012, such as during start-up, is stored in nonvolatile memory 1022. Byway of illustration, and not limitation, nonvolatile memory 1022 caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable programmable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory 1020 includes random accessmemory (RAM), which acts as external cache memory. By way ofillustration and not limitation, RAM is available in many forms such asstatic RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), doubledata rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM(SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM),and Rambus dynamic RAM.

Computer 1012 also includes removable/non-removable,volatile/non-volatile computer storage media. FIG. 10 illustrates, forexample, a disk storage 1024. Disk storage 1024 includes, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memorystick. The disk storage 1024 also can include storage media separatelyor in combination with other storage media including, but not limitedto, an optical disk drive such as a compact disk ROM device (CD-ROM), CDrecordable drive (CD-R Drive), CD rewritable drive (CD-RW Drive) or adigital versatile disk ROM drive (DVD-ROM). To facilitate connection ofthe disk storage devices 1024 to the system bus 1018, a removable ornon-removable interface is typically used, such as interface 1026.

FIG. 10 also depicts software that acts as an intermediary between usersand the basic computer resources described in the suitable operatingenvironment 1000. Such software includes, for example, an operatingsystem 1028. Operating system 1028, which can be stored on disk storage1024, acts to control and allocate resources of the computer system1012. System applications 1030 take advantage of the management ofresources by operating system 1028 through program modules 1032 andprogram data 1034, e.g., stored either in system memory 1016 or on diskstorage 1024. It is to be appreciated that this disclosure can beimplemented with various operating systems or combinations of operatingsystems.

A user enters commands or information into the computer 1012 throughinput device(s) 1036. Input devices 1036 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1014through the system bus 1018 via interface port(s) 1038. Interfaceport(s) 1038 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1040 usesome of the same type of ports as input device(s) 1036. Thus, forexample, a USB port may be used to provide input to computer 1012, andto output information from computer 1012 to an output device 1040.Output adapter 1042 is provided to illustrate that there are some outputdevices 1040 like monitors, speakers, and printers, among other outputdevices 1040, which require special adapters. The output adapters 1042include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1040and the system bus 1018. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asremote computer(s) 1044.

Computer 1012 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1044. The remote computer(s) 1044 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device or other common network node and the like, and typicallyincludes many or all of the elements described relative to computer1012. For purposes of brevity, only a memory storage device 1046 isillustrated with remote computer(s) 1044. Remote computer(s) 1044 islogically connected to computer 1012 through a network interface 1048and then physically connected via communication connection 1050. Networkinterface 1048 encompasses wire and/or wireless communication networkssuch as local-area networks (LAN), wide-area networks (WAN), cellularnetworks, etc. LAN technologies include Fiber Distributed Data Interface(FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ringand the like. WAN technologies include, but are not limited to,point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL).

Communication connection(s) 1050 refers to the hardware/softwareemployed to connect the network interface 1048 to the bus 1018. Whilecommunication connection 1050 is shown for illustrative clarity insidecomputer 1012, it can also be external to computer 1012. Thehardware/software necessary for connection to the network interface 1048includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and Ethernet cards.

FIG. 11 is a schematic block diagram of a sample-computing environment1100 with which the subject matter of this disclosure can interact. Thesystem 1100 includes one or more client(s) 1110. The client(s) 1110 canbe hardware and/or software (e.g., threads, processes, computingdevices). The system 1100 also includes one or more server(s) 1130.Thus, system 1100 can correspond to a two-tier client server model or amulti-tier model (e.g., client, middle tier server, data server),amongst other models. The server(s) 1130 can also be hardware and/orsoftware (e.g., threads, processes, computing devices). The servers 1130can house threads to perform transformations by employing thisdisclosure, for example. One possible communication between a client1110 and a server 1130 may be in the form of a data packet transmittedbetween two or more computer processes.

The system 1100 includes a communication framework 1150 that can beemployed to facilitate communications between the client(s) 1110 and theserver(s) 1130. The client(s) 1110 are operatively connected to one ormore client data store(s) 1120 that can be employed to store informationlocal to the client(s) 1110. Similarly, the server(s) 1130 areoperatively connected to one or more server data store(s) 1140 that canbe employed to store information local to the servers 1130.

It is to be noted that aspects or features of this disclosure can beexploited in substantially any wireless telecommunication or radiotechnology, e.g., Wi-Fi; Bluetooth; Worldwide Interoperability forMicrowave Access (WiMAX); Enhanced General Packet Radio Service(Enhanced GPRS); Third Generation Partnership Project (3GPP) Long TermEvolution (LTE); Third Generation Partnership Project 2 (3GPP2) UltraMobile Broadband (UMB); 3GPP Universal Mobile Telecommunication System(UMTS); High Speed Packet Access (HSPA); High Speed Downlink PacketAccess (HSDPA); High Speed Uplink Packet Access (HSUPA); GSM (GlobalSystem for Mobile Communications) EDGE (Enhanced Data Rates for GSMEvolution) Radio Access Network (GERAN); UMTS Terrestrial Radio AccessNetwork (UTRAN); LTE Advanced (LTE-A); etc. Additionally, some or all ofthe aspects described herein can be exploited in legacytelecommunication technologies, e.g., GSM. In addition, mobile as wellnon-mobile networks (e.g., the Internet, data service network such asinternet protocol television (IPTV), etc.) can exploit aspects orfeatures described herein.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthis disclosure also can or may be implemented in combination with otherprogram modules. Generally, program modules include routines, programs,components, data structures, etc. that perform particular tasks and/orimplement particular abstract data types. Moreover, those skilled in theart will appreciate that the inventive methods may be practiced withother computer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as personal computers, hand-held computing devices(e.g., PDA, phone), microprocessor-based or programmable consumer orindustrial electronics, and the like. The illustrated aspects may alsobe practiced in distributed computing environments where tasks areperformed by remote processing devices that are linked through acommunications network. However, some, if not all aspects of thisdisclosure can be practiced on stand-alone computers. In a distributedcomputing environment, program modules may be located in both local andremote memory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component may be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers.

In another example, respective components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor. In such acase, the processor can be internal or external to the apparatus and canexecute at least a part of the software or firmware application. As yetanother example, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,wherein the electronic components can include a processor or other meansto execute software or firmware that confers at least in part thefunctionality of the electronic components. In an aspect, a componentcan emulate an electronic component via a virtual machine, e.g., withina cloud computing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form.

As used herein, the terms “example” and/or “exemplary” are utilized tomean serving as an example, instance, or illustration. For the avoidanceof doubt, the subject matter disclosed herein is not limited by suchexamples. In addition, any aspect or design described herein as an“example” and/or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent exemplary structures and techniques known tothose of ordinary skill in the art.

Various aspects or features described herein can be implemented as amethod, apparatus, system, or article of manufacture using standardprogramming or engineering techniques. In addition, various aspects orfeatures disclosed in this disclosure can be realized through programmodules that implement at least one or more of the methods disclosedherein, the program modules being stored in a memory and executed by atleast a processor. Other combinations of hardware and software orhardware and firmware can enable or implement aspects described herein,including a disclosed method(s). The term “article of manufacture” asused herein can encompass a computer program accessible from anycomputer-readable device, carrier, or storage media. For example,computer readable storage media can include but are not limited tomagnetic storage devices (e.g., hard disk, floppy disk, magnetic strips. . . ), optical discs (e.g., compact disc (CD), digital versatile disc(DVD), blu-ray disc (BD) . . . ), smart cards, and flash memory devices(e.g., card, stick, key drive . . . ), or the like.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor may also beimplemented as a combination of computing processing units.

In this disclosure, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory.

By way of illustration, and not limitation, nonvolatile memory caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable ROM (EEPROM), flashmemory, or nonvolatile random access memory (RAM) (e.g., ferroelectricRAM (FeRAM). Volatile memory can include RAM, which can act as externalcache memory, for example. By way of illustration and not limitation,RAM is available in many forms such as synchronous RAM (SRAM), dynamicRAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), direct RambusRAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM(RDRAM). Additionally, the disclosed memory components of systems ormethods herein are intended to include, without being limited toincluding, these and any other suitable types of memory.

It is to be appreciated and understood that components, as describedwith regard to a particular system or method, can include the same orsimilar functionality as respective components (e.g., respectively namedcomponents or similarly named components) as described with regard toother systems or methods disclosed herein.

What has been described above includes examples of systems and methodsthat provide advantages of this disclosure. It is, of course, notpossible to describe every conceivable combination of components ormethods for purposes of describing this disclosure, but one of ordinaryskill in the art may recognize that many further combinations andpermutations of this disclosure are possible. Furthermore, to the extentthat the terms “includes,” “has,” “possesses,” and the like are used inthe detailed description, claims, appendices and drawings such terms areintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

What is claimed is:
 1. A system, comprising: a memory that storescomputer executable components; and a processor that executes thecomputer executable components stored in the memory, wherein thecomputer executable components comprise: a multi-channel generatorcomponent that generates: synthetic multi-channel data associated with asynthetic image, a first data channel associated with a synthetic image,a second data channel associated with first segmentation data indicativeof a segmentation for the synthetic image, and a third data channelassociated with second segmentation data indicative of a remainingsegmentation for the synthetic image; and a post-processing componentthat generates first mask data indicative of a first ground truth maskfor the first segmentation data, and second mask data indicative of asecond ground truth mask for the second segmentation data, wherein thefirst mask data comprises a binary weight for segmentation in an imageassociated with the synthetic image.
 2. The system of claim 1, whereinthe computer executable components further comprise: a synthetic imagegenerator component that generates the synthetic image.
 3. The system ofclaim 1, wherein the computer executable components further comprise: asegmentation component that generates the first segmentation dataassociated with the second data channel and the second segmentation dataassociated with the third data channel.
 4. The system of claim 1,wherein the multi-channel generator component generates the syntheticmulti-channel data based on a data distribution.
 5. The system of claim4, wherein the data distribution is associated with a random vector. 6.The system of claim 1, wherein the multi-channel generator componentgenerates the synthetic multi-channel data based on at least one latentrandom variable.
 7. The system of claim 1, wherein the multi-channelgenerator component employs a deep neural network to generate thesynthetic multi-channel data.
 8. The system of claim 1, wherein thefirst mask data comprises a binary mask that corresponds to anannotation associated with the first segmentation data indicative of thesegmentation for the synthetic image.
 9. The system of claim 8, whereinthe second mask data comprises a binary mask that corresponds to anannotation associated with the first segmentation data indicative of theremaining segmentation for the synthetic image.
 10. The system of claim1, wherein the first mask data comprises a filter to mask one or moreregions in the synthetic image.
 11. The system of claim 1, wherein thefirst mask data comprises a group of pixels that define a location forsegmentation associated with an image, wherein the image is associatedwith the synthetic image.
 12. A method, comprising: generating, by asystem comprising a processor, synthetic multi-channel data associatedwith a synthetic image, comprising: generating, by the system, a firstdata channel associated with the synthetic image, generating, by thesystem, a second data channel associated with first segmentation dataindicative of a segmentation for the synthetic image, and generating, bythe system, a third data channel associated with second segmentationdata indicative of a remaining segmentation for the synthetic image;generating, by the system, first mask data indicative of a first groundtruth mask for the first segmentation data, wherein the first mask datacomprises a binary weight for segmentation in an image associated withthe synthetic image; and generating, by the system, second mask dataindicative of a second ground truth mask for the second segmentationdata.
 13. The method of claim 12, wherein generating the syntheticmulti-channel data comprises generating the synthetic multi-channel databased on a data distribution.
 14. The method of claim 13, wherein thedata distribution is associated with a random vector.
 15. The method ofclaim 12, wherein generating the synthetic multi-channel data comprisesgenerating the synthetic multi-channel data based on at least one latentrandom variable.
 16. The method of claim 12, wherein generating thesynthetic multi-channel data comprises using a deep neural network togenerate the synthetic multi-channel data.
 17. A non-transitorymachine-readable medium, comprising executable instructions that, whenexecuted by a processor, facilitate performance of operations,comprising: generating a first data channel associated with a syntheticimage; generating a second data channel associated with firstsegmentation data indicative of a segmentation for the synthetic image;generating a third data channel associated with second segmentation dataindicative of a remaining segmentation for the synthetic image;generating first mask data indicative of a first ground truth mask forthe first segmentation data, wherein the first mask data comprises abinary weight for segmentation in an image associated with the syntheticimage and generating second mask data indicative of a second groundtruth mask for the second segmentation data.
 18. The non-transitorymachine-readable medium of claim 17, wherein the first mask datacomprises a filter to mask one or more regions in the synthetic image.19. The non-transitory machine-readable medium of claim 17, wherein thefirst mask data comprises a binary mask that corresponds to anannotation associated with the first segmentation data indicative of thesegmentation for the synthetic image.
 20. The non-transitorymachine-readable medium of claim 19, wherein the second mask datacomprises a binary mask that corresponds to an annotation associatedwith the first segmentation data indicative of the remainingsegmentation for the synthetic image.